Who is this presentation for?

Prerequisite knowledge

Familiarity with performance monitoring and troubleshooting (useful but not required)

What you'll learn

Explore digital signal processing concepts and learn how these principles are leveraged by organizations to improve the fidelity of their performance monitoring

Description

Over the past 15 years, Jon Hodgson has helped companies all over the world solve performance issues in their most critical applications. One recurring theme Jon has encountered is that inadequate monitoring data either masks or misrepresents the true symptom or root cause, leading their troubleshooters on a futile quest that never leads to a resolution. One of the main culprits behind this is aliasing, artifacts and errors which occur when a signal is improperly sampled. You’ve likely encountered this phenomenon in low-quality digital audio, image, and video files, which are distorted when compared to their original source.

The digital media industry leverages fundamental digital signal processing principles to ensure that analog events are captured with precise fidelity, yet in the field of performance monitoring, these principles seem to be largely absent in many tools, which then imprecisely capture the performance data we rely on.

Using sound as a surrogate for monitoring data, Jon offers an overview of digital signal processing concepts and explores analogous real-world cases where performance data effectively “lied” to the troubleshooter, suggesting that overloaded resources weren’t and vice versa. You’ll learn how to avoid these pitfalls and determine if the data from your monitoring tools is sufficient to detect and quantify certain common problems and how to read between the lines to identify when your data can (and cannot) be trusted. Along the way, Jon explains how other fields have overcome these challenges and how some of their unconventional techniques can be applied to DevOps methodologies.

This session is sponsored by Riverbed.

Jon Hodgson

Riverbed

Jon Hodgson is the principal scientist for APM at Riverbed Technology. For over a decade, Jon has helped hundreds of organizations around the world optimize the reliability and performance of their mission-critical applications. With a background in data science, application architecture, systems administration, networking and programming, Jon employs a multidisciplinary approach to troubleshooting, enabling him to analyze and solve some of the most challenging performance issues in complex modern environments. When he’s not obsessing about data visualization and making things perform faster, Jon enjoys digging things up with his tractor at his home in Missouri.